Consolidate Robot and CNC Controllers in a Single Real-time Windows IPC

Demo video: How to consolidate robot and CNC controllers into a single real-time Windows IPC

1 min read
Kingstar

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When it comes to machine automation, hardware costs and complexity add up fast. As your requirements expand, so does your ever-growing list of hardware: a controller for robot control. Another for CNC. Another for machine vision. You end up with a lot of controllers, perhaps even proprietary, ultimately end up being a lot of systems to manage – and a lot of dollars out of your pocket.

Software-based machine control changes that paradigm. With the right software and a single real-time Windows PC, you can consolidate all of those controllers and their associated costs. Your Windows IPC becomes the only controller that you need. Simply by flipping a switch or moving an Ethernet cable, you can seamlessly switch from a robot controller to CNC controller to a GigE camera. No more separate infrastructure with separate costs, no need for data acquisition or control cards – just one integrated real-time Windows machine acting as an all-in-one controller.

What about the challenges of EtherCAT? While EtherCAT is recognized as the network standard for software motion control, it’s not without issues. That’s why KINGSTAR delivers auto-discovery, auto-configuration, and much more, all in a “plug-and-play,” open and standards-based environment.

Software-based machine automation also supports the modern needs of Industry 4.0 and the Industrial IoT (IIoT). It enables an OPC UA connection to the cloud for analytics, back-end needs, and security. A SCADA connection saves data to your database for real-time processing. And with new add-ons consistently added to the platform, you can keep up with the industry’s move to the cloud.

Watch the demo video below to learn how KINGSTAR helps you radically simplify your architecture by consolidating controllers and modernizing machine automation.

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How the U.S. Army Is Turning Robots Into Team Players

Engineers battle the limits of deep learning for battlefield bots

11 min read
Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

“I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

This article is part of our special report on AI, “The Great AI Reckoning.”

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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